Model 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
cat(paste0(" > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
# Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
TRAITS.TARGET.RANK[target_of_interest],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
* Analyzing the effect of plaque target-of-interest on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
319 304
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4,5,6,7,8,9,10,11,12,13,14 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 3,4,5,6,7,8,9,10,11,12,13,16 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 620, number of events= 80
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -3.351e-01 7.153e-01 2.322e-01 -1.443 0.1491
Age 3.283e-02 1.033e+00 1.377e-02 2.384 0.0171 *
Gendermale 7.816e-01 2.185e+00 3.270e-01 2.390 0.0168 *
ORdate_year2002 1.627e+01 1.165e+07 8.569e+03 0.002 0.9985
ORdate_year2003 1.513e+01 3.712e+06 8.569e+03 0.002 0.9986
ORdate_year2004 1.549e+01 5.320e+06 8.569e+03 0.002 0.9986
ORdate_year2005 1.582e+01 7.424e+06 8.569e+03 0.002 0.9985
ORdate_year2006 1.582e+01 7.416e+06 8.569e+03 0.002 0.9985
ORdate_year2007 1.497e+01 3.188e+06 8.569e+03 0.002 0.9986
ORdate_year2008 1.600e+01 8.864e+06 8.569e+03 0.002 0.9985
ORdate_year2009 1.533e+01 4.547e+06 8.569e+03 0.002 0.9986
ORdate_year2010 1.567e+01 6.404e+06 8.569e+03 0.002 0.9985
ORdate_year2011 1.475e+01 2.543e+06 8.569e+03 0.002 0.9986
ORdate_year2012 1.542e+01 4.972e+06 8.569e+03 0.002 0.9986
ORdate_year2013 -8.764e-01 4.163e-01 9.370e+03 0.000 0.9999
ORdate_year2014 -5.470e-01 5.787e-01 9.846e+03 0.000 1.0000
ORdate_year2015 1.640e-01 1.178e+00 9.788e+03 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 7.153e-01 1.398e+00 0.4537 1.128
Age 1.033e+00 9.677e-01 1.0059 1.062
Gendermale 2.185e+00 4.577e-01 1.1511 4.148
ORdate_year2002 1.165e+07 8.586e-08 0.0000 Inf
ORdate_year2003 3.712e+06 2.694e-07 0.0000 Inf
ORdate_year2004 5.320e+06 1.880e-07 0.0000 Inf
ORdate_year2005 7.424e+06 1.347e-07 0.0000 Inf
ORdate_year2006 7.416e+06 1.348e-07 0.0000 Inf
ORdate_year2007 3.188e+06 3.136e-07 0.0000 Inf
ORdate_year2008 8.864e+06 1.128e-07 0.0000 Inf
ORdate_year2009 4.547e+06 2.199e-07 0.0000 Inf
ORdate_year2010 6.404e+06 1.561e-07 0.0000 Inf
ORdate_year2011 2.543e+06 3.933e-07 0.0000 Inf
ORdate_year2012 4.972e+06 2.011e-07 0.0000 Inf
ORdate_year2013 4.163e-01 2.402e+00 0.0000 Inf
ORdate_year2014 5.787e-01 1.728e+00 0.0000 Inf
ORdate_year2015 1.178e+00 8.488e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.656 (se = 0.029 )
Likelihood ratio test= 28.81 on 17 df, p=0.04
Wald test = 23.66 on 17 df, p=0.1
Score (logrank) test = 26.46 on 17 df, p=0.07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epmajor.3years
Protein...................: HDAC9
Effect size...............: -0.335091
Standard error............: 0.232242
Odds ratio (effect size)..: 0.715
Lower 95% CI..............: 0.454
Upper 95% CI..............: 1.128
T-value...................: -1.442853
P-value...................: 0.1490618
Sample size in model......: 620
Number of events..........: 80
* Analyzing the effect of plaque target-of-interest on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
319 304
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4,5,6,7,8,9,10,11,12,13,14 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 3,4,5,6,7,8,9,10,11,12,13 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 620, number of events= 47
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -3.870e-01 6.791e-01 3.038e-01 -1.274 0.203
Age 2.669e-02 1.027e+00 1.780e-02 1.499 0.134
Gendermale 3.709e-01 1.449e+00 3.741e-01 0.991 0.322
ORdate_year2002 1.657e+01 1.571e+07 1.150e+04 0.001 0.999
ORdate_year2003 1.557e+01 5.754e+06 1.150e+04 0.001 0.999
ORdate_year2004 1.575e+01 6.918e+06 1.150e+04 0.001 0.999
ORdate_year2005 1.625e+01 1.136e+07 1.150e+04 0.001 0.999
ORdate_year2006 1.650e+01 1.465e+07 1.150e+04 0.001 0.999
ORdate_year2007 1.501e+01 3.297e+06 1.150e+04 0.001 0.999
ORdate_year2008 1.637e+01 1.292e+07 1.150e+04 0.001 0.999
ORdate_year2009 1.506e+01 3.460e+06 1.150e+04 0.001 0.999
ORdate_year2010 1.641e+01 1.343e+07 1.150e+04 0.001 0.999
ORdate_year2011 1.502e+01 3.329e+06 1.150e+04 0.001 0.999
ORdate_year2012 1.602e+01 9.024e+06 1.150e+04 0.001 0.999
ORdate_year2013 -4.458e-01 6.403e-01 1.258e+04 0.000 1.000
ORdate_year2014 -1.032e-01 9.019e-01 1.321e+04 0.000 1.000
ORdate_year2015 3.256e-01 1.385e+00 1.324e+04 0.000 1.000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 6.791e-01 1.473e+00 0.3744 1.232
Age 1.027e+00 9.737e-01 0.9918 1.064
Gendermale 1.449e+00 6.901e-01 0.6960 3.017
ORdate_year2002 1.571e+07 6.366e-08 0.0000 Inf
ORdate_year2003 5.754e+06 1.738e-07 0.0000 Inf
ORdate_year2004 6.918e+06 1.446e-07 0.0000 Inf
ORdate_year2005 1.136e+07 8.801e-08 0.0000 Inf
ORdate_year2006 1.465e+07 6.828e-08 0.0000 Inf
ORdate_year2007 3.297e+06 3.033e-07 0.0000 Inf
ORdate_year2008 1.292e+07 7.737e-08 0.0000 Inf
ORdate_year2009 3.460e+06 2.891e-07 0.0000 Inf
ORdate_year2010 1.343e+07 7.444e-08 0.0000 Inf
ORdate_year2011 3.329e+06 3.004e-07 0.0000 Inf
ORdate_year2012 9.024e+06 1.108e-07 0.0000 Inf
ORdate_year2013 6.403e-01 1.562e+00 0.0000 Inf
ORdate_year2014 9.019e-01 1.109e+00 0.0000 Inf
ORdate_year2015 1.385e+00 7.221e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.676 (se = 0.035 )
Likelihood ratio test= 19.37 on 17 df, p=0.3
Wald test = 13.33 on 17 df, p=0.7
Score (logrank) test = 17.81 on 17 df, p=0.4
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epstroke.3years
Protein...................: HDAC9
Effect size...............: -0.38697
Standard error............: 0.303845
Odds ratio (effect size)..: 0.679
Lower 95% CI..............: 0.374
Upper 95% CI..............: 1.232
T-value...................: -1.273579
P-value...................: 0.2028128
Sample size in model......: 620
Number of events..........: 47
* Analyzing the effect of plaque target-of-interest on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
319 304
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4,5,6,7,8,9,10,11,12,13,14 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 3,4,5,6,7,8,9,10,11,12,13 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 620, number of events= 48
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -3.056e-01 7.367e-01 2.966e-01 -1.030 0.3029
Age 1.465e-03 1.001e+00 1.721e-02 0.085 0.9321
Gendermale 9.151e-01 2.497e+00 4.391e-01 2.084 0.0372 *
ORdate_year2002 1.639e+01 1.313e+07 1.115e+04 0.001 0.9988
ORdate_year2003 1.578e+01 7.114e+06 1.115e+04 0.001 0.9989
ORdate_year2004 1.496e+01 3.140e+06 1.115e+04 0.001 0.9989
ORdate_year2005 1.571e+01 6.629e+06 1.115e+04 0.001 0.9989
ORdate_year2006 1.601e+01 8.936e+06 1.115e+04 0.001 0.9989
ORdate_year2007 1.553e+01 5.546e+06 1.115e+04 0.001 0.9989
ORdate_year2008 1.562e+01 6.058e+06 1.115e+04 0.001 0.9989
ORdate_year2009 1.533e+01 4.545e+06 1.115e+04 0.001 0.9989
ORdate_year2010 1.454e+01 2.063e+06 1.115e+04 0.001 0.9990
ORdate_year2011 1.464e+01 2.285e+06 1.115e+04 0.001 0.9990
ORdate_year2012 1.484e+01 2.782e+06 1.115e+04 0.001 0.9989
ORdate_year2013 -9.184e-01 3.991e-01 1.222e+04 0.000 0.9999
ORdate_year2014 -7.102e-01 4.915e-01 1.285e+04 0.000 1.0000
ORdate_year2015 -1.174e-01 8.892e-01 1.280e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 7.367e-01 1.357e+00 0.4119 1.318
Age 1.001e+00 9.985e-01 0.9682 1.036
Gendermale 2.497e+00 4.005e-01 1.0560 5.905
ORdate_year2002 1.313e+07 7.614e-08 0.0000 Inf
ORdate_year2003 7.114e+06 1.406e-07 0.0000 Inf
ORdate_year2004 3.140e+06 3.185e-07 0.0000 Inf
ORdate_year2005 6.629e+06 1.508e-07 0.0000 Inf
ORdate_year2006 8.936e+06 1.119e-07 0.0000 Inf
ORdate_year2007 5.546e+06 1.803e-07 0.0000 Inf
ORdate_year2008 6.058e+06 1.651e-07 0.0000 Inf
ORdate_year2009 4.545e+06 2.200e-07 0.0000 Inf
ORdate_year2010 2.063e+06 4.848e-07 0.0000 Inf
ORdate_year2011 2.285e+06 4.376e-07 0.0000 Inf
ORdate_year2012 2.782e+06 3.595e-07 0.0000 Inf
ORdate_year2013 3.991e-01 2.505e+00 0.0000 Inf
ORdate_year2014 4.915e-01 2.034e+00 0.0000 Inf
ORdate_year2015 8.892e-01 1.125e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.654 (se = 0.038 )
Likelihood ratio test= 17.21 on 17 df, p=0.4
Wald test = 13.48 on 17 df, p=0.7
Score (logrank) test = 15.71 on 17 df, p=0.5
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epcoronary.3years
Protein...................: HDAC9
Effect size...............: -0.305628
Standard error............: 0.296646
Odds ratio (effect size)..: 0.737
Lower 95% CI..............: 0.412
Upper 95% CI..............: 1.318
T-value...................: -1.030276
P-value...................: 0.3028806
Sample size in model......: 620
Number of events..........: 48
* Analyzing the effect of plaque target-of-interest on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
319 304
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4,5,6,7,8,9,10,11,12 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 3,4,5,6,7,8,9,10,11 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 620, number of events= 28
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -7.408e-01 4.768e-01 4.175e-01 -1.774 0.07603 .
Age 7.826e-02 1.081e+00 2.610e-02 2.999 0.00271 **
Gendermale 6.706e-01 1.955e+00 5.434e-01 1.234 0.21717
ORdate_year2002 1.857e+01 1.158e+08 3.447e+04 0.001 0.99957
ORdate_year2003 1.695e+01 2.296e+07 3.447e+04 0.000 0.99961
ORdate_year2004 1.684e+01 2.054e+07 3.447e+04 0.000 0.99961
ORdate_year2005 1.756e+01 4.239e+07 3.447e+04 0.001 0.99959
ORdate_year2006 1.665e+01 1.701e+07 3.447e+04 0.000 0.99961
ORdate_year2007 1.720e+01 2.962e+07 3.447e+04 0.000 0.99960
ORdate_year2008 1.730e+01 3.257e+07 3.447e+04 0.001 0.99960
ORdate_year2009 1.758e+01 4.302e+07 3.447e+04 0.001 0.99959
ORdate_year2010 1.749e+01 3.927e+07 3.447e+04 0.001 0.99960
ORdate_year2011 -5.884e-01 5.552e-01 3.487e+04 0.000 0.99999
ORdate_year2012 -6.736e-01 5.099e-01 3.507e+04 0.000 0.99998
ORdate_year2013 -9.400e-01 3.906e-01 3.728e+04 0.000 0.99998
ORdate_year2014 -1.580e-01 8.539e-01 3.894e+04 0.000 1.00000
ORdate_year2015 7.661e-01 2.151e+00 3.907e+04 0.000 0.99998
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 4.768e-01 2.098e+00 0.2103 1.081
Age 1.081e+00 9.247e-01 1.0275 1.138
Gendermale 1.955e+00 5.114e-01 0.6740 5.673
ORdate_year2002 1.158e+08 8.639e-09 0.0000 Inf
ORdate_year2003 2.296e+07 4.356e-08 0.0000 Inf
ORdate_year2004 2.054e+07 4.868e-08 0.0000 Inf
ORdate_year2005 4.239e+07 2.359e-08 0.0000 Inf
ORdate_year2006 1.701e+07 5.880e-08 0.0000 Inf
ORdate_year2007 2.962e+07 3.376e-08 0.0000 Inf
ORdate_year2008 3.257e+07 3.070e-08 0.0000 Inf
ORdate_year2009 4.302e+07 2.325e-08 0.0000 Inf
ORdate_year2010 3.927e+07 2.546e-08 0.0000 Inf
ORdate_year2011 5.552e-01 1.801e+00 0.0000 Inf
ORdate_year2012 5.099e-01 1.961e+00 0.0000 Inf
ORdate_year2013 3.906e-01 2.560e+00 0.0000 Inf
ORdate_year2014 8.539e-01 1.171e+00 0.0000 Inf
ORdate_year2015 2.151e+00 4.648e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.792 (se = 0.037 )
Likelihood ratio test= 30.55 on 17 df, p=0.02
Wald test = 14.69 on 17 df, p=0.6
Score (logrank) test = 30.08 on 17 df, p=0.03
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epcvdeath.3years
Protein...................: HDAC9
Effect size...............: -0.740749
Standard error............: 0.417516
Odds ratio (effect size)..: 0.477
Lower 95% CI..............: 0.21
Upper 95% CI..............: 1.081
T-value...................: -1.77418
P-value...................: 0.07603341
Sample size in model......: 620
Number of events..........: 28

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AERNASE.clin.hdac9.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL1.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
rowNames = FALSE, colNames = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)
Model 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
cat(paste0(" > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
# Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
TRAITS.TARGET.RANK[target_of_interest],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
* Analyzing the effect of plaque target-of-interest on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
319 304
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4,5,6,7,8,9,10,11,12,13,14,35,36,37 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 3,4,5,6,7,8,9,10,11,12,13,34,35,36 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 535, number of events= 65
(88 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -1.070e-01 8.985e-01 2.602e-01 -0.411 0.6808
Age 4.732e-02 1.048e+00 1.856e-02 2.550 0.0108 *
Gendermale 8.189e-01 2.268e+00 3.879e-01 2.111 0.0348 *
ORdate_year2002 1.536e+01 4.678e+06 1.019e+04 0.002 0.9988
ORdate_year2003 1.429e+01 1.601e+06 1.019e+04 0.001 0.9989
ORdate_year2004 1.475e+01 2.550e+06 1.019e+04 0.001 0.9988
ORdate_year2005 1.523e+01 4.116e+06 1.019e+04 0.001 0.9988
ORdate_year2006 1.520e+01 4.007e+06 1.019e+04 0.001 0.9988
ORdate_year2007 1.422e+01 1.495e+06 1.019e+04 0.001 0.9989
ORdate_year2008 1.528e+01 4.311e+06 1.019e+04 0.001 0.9988
ORdate_year2009 1.418e+01 1.446e+06 1.019e+04 0.001 0.9989
ORdate_year2010 1.508e+01 3.550e+06 1.019e+04 0.001 0.9988
ORdate_year2011 1.440e+01 1.795e+06 1.019e+04 0.001 0.9989
ORdate_year2012 1.498e+01 3.205e+06 1.019e+04 0.001 0.9988
ORdate_year2013 -1.394e+00 2.480e-01 1.121e+04 0.000 0.9999
ORdate_year2014 -9.861e-01 3.730e-01 1.441e+04 0.000 0.9999
ORdate_year2015 -7.423e-01 4.760e-01 1.128e+04 0.000 0.9999
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -6.272e-01 5.341e-01 5.432e-01 -1.155 0.2482
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 6.858e-01 1.985e+00 2.800e-01 2.449 0.0143 *
SmokerStatusEx-smoker -5.207e-01 5.941e-01 2.781e-01 -1.873 0.0611 .
SmokerStatusNever smoked -8.051e-01 4.470e-01 4.407e-01 -1.827 0.0677 .
Med.Statin.LLDno 2.396e-01 1.271e+00 2.979e-01 0.804 0.4213
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.650e-01 1.179e+00 4.249e-01 0.388 0.6978
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -7.953e-03 9.921e-01 6.443e-03 -1.234 0.2170
BMI 5.476e-02 1.056e+00 3.163e-02 1.731 0.0834 .
MedHx_CVDNo -5.580e-01 5.724e-01 2.995e-01 -1.863 0.0624 .
stenose0-49% 4.153e-01 1.515e+00 1.156e+04 0.000 1.0000
stenose50-70% 1.610e+01 9.869e+06 1.019e+04 0.002 0.9987
stenose70-90% 1.663e+01 1.669e+07 1.019e+04 0.002 0.9987
stenose90-99% 1.671e+01 1.802e+07 1.019e+04 0.002 0.9987
stenose100% (Occlusion) 1.043e+00 2.837e+00 1.113e+04 0.000 0.9999
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 8.985e-01 1.113e+00 0.5396 1.496
Age 1.048e+00 9.538e-01 1.0110 1.087
Gendermale 2.268e+00 4.409e-01 1.0603 4.851
ORdate_year2002 4.678e+06 2.138e-07 0.0000 Inf
ORdate_year2003 1.601e+06 6.247e-07 0.0000 Inf
ORdate_year2004 2.550e+06 3.922e-07 0.0000 Inf
ORdate_year2005 4.116e+06 2.430e-07 0.0000 Inf
ORdate_year2006 4.007e+06 2.495e-07 0.0000 Inf
ORdate_year2007 1.495e+06 6.687e-07 0.0000 Inf
ORdate_year2008 4.311e+06 2.320e-07 0.0000 Inf
ORdate_year2009 1.446e+06 6.915e-07 0.0000 Inf
ORdate_year2010 3.550e+06 2.817e-07 0.0000 Inf
ORdate_year2011 1.795e+06 5.571e-07 0.0000 Inf
ORdate_year2012 3.205e+06 3.120e-07 0.0000 Inf
ORdate_year2013 2.480e-01 4.033e+00 0.0000 Inf
ORdate_year2014 3.730e-01 2.681e+00 0.0000 Inf
ORdate_year2015 4.760e-01 2.101e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 5.341e-01 1.872e+00 0.1842 1.549
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.985e+00 5.037e-01 1.1468 3.437
SmokerStatusEx-smoker 5.941e-01 1.683e+00 0.3445 1.025
SmokerStatusNever smoked 4.470e-01 2.237e+00 0.1885 1.060
Med.Statin.LLDno 1.271e+00 7.870e-01 0.7087 2.279
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.179e+00 8.479e-01 0.5128 2.712
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.921e-01 1.008e+00 0.9796 1.005
BMI 1.056e+00 9.467e-01 0.9928 1.124
MedHx_CVDNo 5.724e-01 1.747e+00 0.3183 1.029
stenose0-49% 1.515e+00 6.601e-01 0.0000 Inf
stenose50-70% 9.869e+06 1.013e-07 0.0000 Inf
stenose70-90% 1.669e+07 5.991e-08 0.0000 Inf
stenose90-99% 1.802e+07 5.550e-08 0.0000 Inf
stenose100% (Occlusion) 2.837e+00 3.525e-01 0.0000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.764 (se = 0.025 )
Likelihood ratio test= 55.11 on 31 df, p=0.005
Wald test = 38.75 on 31 df, p=0.2
Score (logrank) test = 53.61 on 31 df, p=0.007
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epmajor.3years
Protein...................: HDAC9
Effect size...............: -0.10702
Standard error............: 0.260153
Odds ratio (effect size)..: 0.899
Lower 95% CI..............: 0.54
Upper 95% CI..............: 1.496
T-value...................: -0.411374
P-value...................: 0.6807986
Sample size in model......: 535
Number of events..........: 65
* Analyzing the effect of plaque target-of-interest on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
319 304
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4,5,6,7,8,9,10,12,13,14,35,36,37 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 3,4,5,6,7,8,9,11,12,13,34,35,36 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 535, number of events= 34
(88 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -1.405e-01 8.689e-01 3.612e-01 -0.389 0.6973
Age 5.009e-02 1.051e+00 2.480e-02 2.020 0.0434 *
Gendermale 4.427e-01 1.557e+00 4.682e-01 0.945 0.3444
ORdate_year2002 1.745e+01 3.778e+07 4.162e+04 0.000 0.9997
ORdate_year2003 1.639e+01 1.318e+07 4.162e+04 0.000 0.9997
ORdate_year2004 1.676e+01 1.897e+07 4.162e+04 0.000 0.9997
ORdate_year2005 1.730e+01 3.260e+07 4.162e+04 0.000 0.9997
ORdate_year2006 1.782e+01 5.483e+07 4.162e+04 0.000 0.9997
ORdate_year2007 1.574e+01 6.883e+06 4.162e+04 0.000 0.9997
ORdate_year2008 1.732e+01 3.332e+07 4.162e+04 0.000 0.9997
ORdate_year2009 -1.313e+00 2.691e-01 4.190e+04 0.000 1.0000
ORdate_year2010 1.758e+01 4.300e+07 4.162e+04 0.000 0.9997
ORdate_year2011 1.647e+01 1.420e+07 4.162e+04 0.000 0.9997
ORdate_year2012 1.725e+01 3.089e+07 4.162e+04 0.000 0.9997
ORdate_year2013 -1.102e+00 3.321e-01 4.614e+04 0.000 1.0000
ORdate_year2014 -9.220e-01 3.977e-01 5.885e+04 0.000 1.0000
ORdate_year2015 -9.160e-01 4.001e-01 4.734e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.841e-01 8.319e-01 6.509e-01 -0.283 0.7773
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 3.340e-01 1.397e+00 4.180e-01 0.799 0.4243
SmokerStatusEx-smoker -5.879e-01 5.555e-01 3.743e-01 -1.571 0.1162
SmokerStatusNever smoked -1.568e+00 2.085e-01 7.651e-01 -2.049 0.0404 *
Med.Statin.LLDno 2.917e-01 1.339e+00 4.067e-01 0.717 0.4733
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -6.841e-02 9.339e-01 6.452e-01 -0.106 0.9156
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 6.356e-03 1.006e+00 8.677e-03 0.732 0.4639
BMI 9.627e-02 1.101e+00 4.095e-02 2.351 0.0187 *
MedHx_CVDNo -4.748e-01 6.220e-01 4.047e-01 -1.173 0.2406
stenose0-49% 1.588e+00 4.896e+00 4.579e+04 0.000 1.0000
stenose50-70% 1.952e+01 2.998e+08 4.161e+04 0.000 0.9996
stenose70-90% 1.942e+01 2.725e+08 4.161e+04 0.000 0.9996
stenose90-99% 1.951e+01 2.959e+08 4.161e+04 0.000 0.9996
stenose100% (Occlusion) 1.617e+00 5.039e+00 4.436e+04 0.000 1.0000
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 8.689e-01 1.151e+00 0.42810 1.7636
Age 1.051e+00 9.511e-01 1.00148 1.1037
Gendermale 1.557e+00 6.423e-01 0.62185 3.8978
ORdate_year2002 3.778e+07 2.647e-08 0.00000 Inf
ORdate_year2003 1.318e+07 7.589e-08 0.00000 Inf
ORdate_year2004 1.897e+07 5.272e-08 0.00000 Inf
ORdate_year2005 3.260e+07 3.068e-08 0.00000 Inf
ORdate_year2006 5.483e+07 1.824e-08 0.00000 Inf
ORdate_year2007 6.883e+06 1.453e-07 0.00000 Inf
ORdate_year2008 3.332e+07 3.001e-08 0.00000 Inf
ORdate_year2009 2.691e-01 3.716e+00 0.00000 Inf
ORdate_year2010 4.300e+07 2.325e-08 0.00000 Inf
ORdate_year2011 1.420e+07 7.044e-08 0.00000 Inf
ORdate_year2012 3.089e+07 3.237e-08 0.00000 Inf
ORdate_year2013 3.321e-01 3.011e+00 0.00000 Inf
ORdate_year2014 3.977e-01 2.514e+00 0.00000 Inf
ORdate_year2015 4.001e-01 2.499e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 8.319e-01 1.202e+00 0.23227 2.9793
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.397e+00 7.161e-01 0.61551 3.1686
SmokerStatusEx-smoker 5.555e-01 1.800e+00 0.26673 1.1568
SmokerStatusNever smoked 2.085e-01 4.797e+00 0.04654 0.9339
Med.Statin.LLDno 1.339e+00 7.470e-01 0.60326 2.9706
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 9.339e-01 1.071e+00 0.26371 3.3071
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.006e+00 9.937e-01 0.98941 1.0236
BMI 1.101e+00 9.082e-01 1.01613 1.1931
MedHx_CVDNo 6.220e-01 1.608e+00 0.28141 1.3748
stenose0-49% 4.896e+00 2.043e-01 0.00000 Inf
stenose50-70% 2.998e+08 3.335e-09 0.00000 Inf
stenose70-90% 2.725e+08 3.669e-09 0.00000 Inf
stenose90-99% 2.959e+08 3.379e-09 0.00000 Inf
stenose100% (Occlusion) 5.039e+00 1.985e-01 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.795 (se = 0.031 )
Likelihood ratio test= 39.43 on 31 df, p=0.1
Wald test = 16.75 on 31 df, p=1
Score (logrank) test = 35.86 on 31 df, p=0.3
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epstroke.3years
Protein...................: HDAC9
Effect size...............: -0.140508
Standard error............: 0.361176
Odds ratio (effect size)..: 0.869
Lower 95% CI..............: 0.428
Upper 95% CI..............: 1.764
T-value...................: -0.38903
P-value...................: 0.6972542
Sample size in model......: 535
Number of events..........: 34
* Analyzing the effect of plaque target-of-interest on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
319 304
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4,5,6,7,8,9,10,11,12,13,14,16,17,35,36,37,38 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 3,4,5,6,7,8,9,10,11,12,13,35,36,37 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 535, number of events= 43
(88 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -1.546e-01 8.567e-01 3.205e-01 -0.482 0.62953
Age -1.299e-03 9.987e-01 2.280e-02 -0.057 0.95459
Gendermale 1.273e+00 3.572e+00 5.389e-01 2.362 0.01816 *
ORdate_year2002 1.746e+01 3.822e+07 3.427e+04 0.001 0.99959
ORdate_year2003 1.707e+01 2.601e+07 3.427e+04 0.000 0.99960
ORdate_year2004 1.624e+01 1.132e+07 3.427e+04 0.000 0.99962
ORdate_year2005 1.722e+01 3.015e+07 3.427e+04 0.001 0.99960
ORdate_year2006 1.702e+01 2.464e+07 3.427e+04 0.000 0.99960
ORdate_year2007 1.684e+01 2.054e+07 3.427e+04 0.000 0.99961
ORdate_year2008 1.697e+01 2.344e+07 3.427e+04 0.000 0.99960
ORdate_year2009 1.670e+01 1.784e+07 3.427e+04 0.000 0.99961
ORdate_year2010 1.563e+01 6.123e+06 3.427e+04 0.000 0.99964
ORdate_year2011 1.602e+01 9.103e+06 3.427e+04 0.000 0.99963
ORdate_year2012 1.654e+01 1.525e+07 3.427e+04 0.000 0.99961
ORdate_year2013 -1.790e+00 1.669e-01 3.758e+04 0.000 0.99996
ORdate_year2014 -5.827e-01 5.584e-01 4.847e+04 0.000 0.99999
ORdate_year2015 -1.527e-01 8.584e-01 3.747e+04 0.000 1.00000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.679e+00 1.865e-01 1.029e+00 -1.631 0.10283
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 3.192e-01 1.376e+00 3.502e-01 0.912 0.36194
SmokerStatusEx-smoker -3.283e-01 7.201e-01 3.536e-01 -0.929 0.35308
SmokerStatusNever smoked 9.937e-02 1.104e+00 4.722e-01 0.210 0.83332
Med.Statin.LLDno -4.074e-01 6.654e-01 4.369e-01 -0.932 0.35113
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.760e-02 1.018e+00 5.674e-01 0.031 0.97526
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.438e-02 9.857e-01 8.199e-03 -1.754 0.07940 .
BMI 1.769e-02 1.018e+00 4.199e-02 0.421 0.67352
MedHx_CVDNo -1.199e+00 3.016e-01 4.254e-01 -2.818 0.00484 **
stenose0-49% -1.731e+00 1.771e-01 3.919e+04 0.000 0.99996
stenose50-70% -6.636e-01 5.150e-01 3.469e+04 0.000 0.99998
stenose70-90% 1.726e+01 3.129e+07 3.427e+04 0.001 0.99960
stenose90-99% 1.708e+01 2.616e+07 3.427e+04 0.000 0.99960
stenose100% (Occlusion) -5.124e-01 5.990e-01 3.719e+04 0.000 0.99999
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 8.567e-01 1.167e+00 0.4571 1.6057
Age 9.987e-01 1.001e+00 0.9551 1.0443
Gendermale 3.572e+00 2.800e-01 1.2421 10.2700
ORdate_year2002 3.822e+07 2.617e-08 0.0000 Inf
ORdate_year2003 2.601e+07 3.845e-08 0.0000 Inf
ORdate_year2004 1.132e+07 8.835e-08 0.0000 Inf
ORdate_year2005 3.015e+07 3.317e-08 0.0000 Inf
ORdate_year2006 2.464e+07 4.059e-08 0.0000 Inf
ORdate_year2007 2.054e+07 4.869e-08 0.0000 Inf
ORdate_year2008 2.344e+07 4.266e-08 0.0000 Inf
ORdate_year2009 1.784e+07 5.606e-08 0.0000 Inf
ORdate_year2010 6.123e+06 1.633e-07 0.0000 Inf
ORdate_year2011 9.103e+06 1.099e-07 0.0000 Inf
ORdate_year2012 1.525e+07 6.557e-08 0.0000 Inf
ORdate_year2013 1.669e-01 5.992e+00 0.0000 Inf
ORdate_year2014 5.584e-01 1.791e+00 0.0000 Inf
ORdate_year2015 8.584e-01 1.165e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 1.865e-01 5.362e+00 0.0248 1.4026
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.376e+00 7.267e-01 0.6928 2.7333
SmokerStatusEx-smoker 7.201e-01 1.389e+00 0.3601 1.4400
SmokerStatusNever smoked 1.104e+00 9.054e-01 0.4378 2.7866
Med.Statin.LLDno 6.654e-01 1.503e+00 0.2826 1.5667
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.018e+00 9.826e-01 0.3347 3.0945
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.857e-01 1.014e+00 0.9700 1.0017
BMI 1.018e+00 9.825e-01 0.9374 1.1051
MedHx_CVDNo 3.016e-01 3.316e+00 0.1310 0.6943
stenose0-49% 1.771e-01 5.646e+00 0.0000 Inf
stenose50-70% 5.150e-01 1.942e+00 0.0000 Inf
stenose70-90% 3.129e+07 3.196e-08 0.0000 Inf
stenose90-99% 2.616e+07 3.822e-08 0.0000 Inf
stenose100% (Occlusion) 5.990e-01 1.669e+00 0.0000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.778 (se = 0.027 )
Likelihood ratio test= 45.51 on 31 df, p=0.04
Wald test = 23.94 on 31 df, p=0.8
Score (logrank) test = 37.26 on 31 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epcoronary.3years
Protein...................: HDAC9
Effect size...............: -0.15461
Standard error............: 0.320506
Odds ratio (effect size)..: 0.857
Lower 95% CI..............: 0.457
Upper 95% CI..............: 1.606
T-value...................: -0.482393
P-value...................: 0.6295269
Sample size in model......: 535
Number of events..........: 43
* Analyzing the effect of plaque target-of-interest on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [HDAC9]; 1 out of 1 target-of-interest.
> cross tabulation of HDAC9-stratum.
[ 0, 22) [22,449]
319 304
> fitting the model for HDAC9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4,5,6,7,8,9,10,11,12,13,14,15,16,17,22,34,35,36,37,38 ; coefficient may be infinite.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 3,4,5,6,7,8,9,10,11,13,16,21,33,34,35,36,37 ; coefficient may be infinite.


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 535, number of events= 23
(88 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -2.771e-01 7.580e-01 4.600e-01 -0.602 0.5469
Age 7.215e-02 1.075e+00 3.477e-02 2.075 0.0380 *
Gendermale 6.159e-01 1.851e+00 6.573e-01 0.937 0.3487
ORdate_year2002 1.820e+01 8.034e+07 7.481e+04 0.000 0.9998
ORdate_year2003 1.684e+01 2.065e+07 7.481e+04 0.000 0.9998
ORdate_year2004 1.699e+01 2.393e+07 7.481e+04 0.000 0.9998
ORdate_year2005 1.775e+01 5.101e+07 7.481e+04 0.000 0.9998
ORdate_year2006 1.673e+01 1.847e+07 7.481e+04 0.000 0.9998
ORdate_year2007 1.709e+01 2.642e+07 7.481e+04 0.000 0.9998
ORdate_year2008 1.726e+01 3.131e+07 7.481e+04 0.000 0.9998
ORdate_year2009 1.691e+01 2.214e+07 7.481e+04 0.000 0.9998
ORdate_year2010 1.766e+01 4.657e+07 7.481e+04 0.000 0.9998
ORdate_year2011 -1.550e+00 2.123e-01 7.545e+04 0.000 1.0000
ORdate_year2012 -1.213e+00 2.974e-01 7.591e+04 0.000 1.0000
ORdate_year2013 -1.558e+00 2.105e-01 8.214e+04 0.000 1.0000
ORdate_year2014 -1.108e+00 3.303e-01 1.058e+05 0.000 1.0000
ORdate_year2015 -1.084e+00 3.383e-01 7.997e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.820e+01 1.248e-08 4.924e+03 -0.004 0.9971
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 9.437e-01 2.570e+00 4.835e-01 1.952 0.0510 .
SmokerStatusEx-smoker -6.808e-01 5.062e-01 4.963e-01 -1.372 0.1701
SmokerStatusNever smoked -1.101e+00 3.325e-01 8.341e-01 -1.320 0.1868
Med.Statin.LLDno 3.260e-01 1.385e+00 4.851e-01 0.672 0.5015
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.089e+00 2.970e+00 5.963e-01 1.826 0.0679 .
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.453e-02 9.758e-01 1.207e-02 -2.032 0.0422 *
BMI 3.188e-02 1.032e+00 6.026e-02 0.529 0.5968
MedHx_CVDNo -5.370e-01 5.845e-01 5.478e-01 -0.980 0.3269
stenose0-49% -3.568e+01 3.197e-16 8.202e+04 0.000 0.9997
stenose50-70% -1.702e+01 4.059e-08 7.607e+04 0.000 0.9998
stenose70-90% -1.733e+01 2.984e-08 7.607e+04 0.000 0.9998
stenose90-99% -1.703e+01 4.016e-08 7.607e+04 0.000 0.9998
stenose100% (Occlusion) -3.299e+01 4.695e-15 7.795e+04 0.000 0.9997
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 7.580e-01 1.319e+00 0.30766 1.8673
Age 1.075e+00 9.304e-01 1.00401 1.1506
Gendermale 1.851e+00 5.401e-01 0.51053 6.7140
ORdate_year2002 8.034e+07 1.245e-08 0.00000 Inf
ORdate_year2003 2.065e+07 4.844e-08 0.00000 Inf
ORdate_year2004 2.393e+07 4.179e-08 0.00000 Inf
ORdate_year2005 5.101e+07 1.960e-08 0.00000 Inf
ORdate_year2006 1.847e+07 5.416e-08 0.00000 Inf
ORdate_year2007 2.642e+07 3.785e-08 0.00000 Inf
ORdate_year2008 3.131e+07 3.194e-08 0.00000 Inf
ORdate_year2009 2.214e+07 4.516e-08 0.00000 Inf
ORdate_year2010 4.657e+07 2.147e-08 0.00000 Inf
ORdate_year2011 2.123e-01 4.710e+00 0.00000 Inf
ORdate_year2012 2.974e-01 3.363e+00 0.00000 Inf
ORdate_year2013 2.105e-01 4.750e+00 0.00000 Inf
ORdate_year2014 3.303e-01 3.027e+00 0.00000 Inf
ORdate_year2015 3.383e-01 2.956e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 1.248e-08 8.016e+07 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 2.570e+00 3.892e-01 0.99604 6.6287
SmokerStatusEx-smoker 5.062e-01 1.975e+00 0.19137 1.3390
SmokerStatusNever smoked 3.325e-01 3.007e+00 0.06484 1.7051
Med.Statin.LLDno 1.385e+00 7.218e-01 0.53540 3.5853
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.970e+00 3.367e-01 0.92302 9.5590
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.758e-01 1.025e+00 0.95295 0.9991
BMI 1.032e+00 9.686e-01 0.91739 1.1618
MedHx_CVDNo 5.845e-01 1.711e+00 0.19978 1.7102
stenose0-49% 3.197e-16 3.128e+15 0.00000 Inf
stenose50-70% 4.059e-08 2.464e+07 0.00000 Inf
stenose70-90% 2.984e-08 3.351e+07 0.00000 Inf
stenose90-99% 4.016e-08 2.490e+07 0.00000 Inf
stenose100% (Occlusion) 4.695e-15 2.130e+14 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.852 (se = 0.033 )
Likelihood ratio test= 42.92 on 31 df, p=0.08
Wald test = 21.37 on 31 df, p=0.9
Score (logrank) test = 39.69 on 31 df, p=0.1
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.hdac9
Outcome analyzed..........: epcvdeath.3years
Protein...................: HDAC9
Effect size...............: -0.277124
Standard error............: 0.46002
Odds ratio (effect size)..: 0.758
Lower 95% CI..............: 0.308
Upper 95% CI..............: 1.867
T-value...................: -0.602416
P-value...................: 0.546897
Sample size in model......: 535
Number of events..........: 23

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AERNASE.clin.hdac9.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL2.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
rowNames = FALSE, colNames = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)